{"id":"https://openalex.org/W3179719546","doi":"https://doi.org/10.23919/ifipnetworking52078.2021.9472814","title":"Few-Shot Class-Adaptive Anomaly Detection with Model-Agnostic Meta-Learning","display_name":"Few-Shot Class-Adaptive Anomaly Detection with Model-Agnostic Meta-Learning","publication_year":2021,"publication_date":"2021-06-21","ids":{"openalex":"https://openalex.org/W3179719546","doi":"https://doi.org/10.23919/ifipnetworking52078.2021.9472814","mag":"3179719546"},"language":"en","primary_location":{"id":"doi:10.23919/ifipnetworking52078.2021.9472814","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ifipnetworking52078.2021.9472814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IFIP Networking Conference (IFIP Networking)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003240981","display_name":"Tongtong Feng","orcid":"https://orcid.org/0000-0003-4734-5607"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tongtong Feng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406584","display_name":"Qi Qi","orcid":"https://orcid.org/0000-0003-0829-4624"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Qi","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100432460","display_name":"Jingyu Wang","orcid":"https://orcid.org/0000-0002-2182-2228"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055685073","display_name":"Jianxin Liao","orcid":"https://orcid.org/0000-0003-1486-0573"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxin Liao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5003240981"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":2.9036,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.91091711,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.843269944190979},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7273027896881104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6305740475654602},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6302674412727356},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.585912823677063},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5756227374076843},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5260167717933655},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.4800735116004944},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4524683654308319},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.4196263551712036},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41264617443084717},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4021616578102112},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.24772074818611145},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.11437219381332397}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.843269944190979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7273027896881104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6305740475654602},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6302674412727356},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.585912823677063},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5756227374076843},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5260167717933655},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.4800735116004944},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4524683654308319},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.4196263551712036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41264617443084717},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4021616578102112},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.24772074818611145},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.11437219381332397},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/ifipnetworking52078.2021.9472814","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ifipnetworking52078.2021.9472814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IFIP Networking Conference (IFIP Networking)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1182600494","display_name":null,"funder_award_id":"2020YFB1807805","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5494884759","display_name":null,"funder_award_id":"62071067,61771068,61872310","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1596717185","https://openalex.org/W2101234009","https://openalex.org/W2472819217","https://openalex.org/W2538940504","https://openalex.org/W2601450892","https://openalex.org/W2604763608","https://openalex.org/W2768211408","https://openalex.org/W2771013472","https://openalex.org/W2789828921","https://openalex.org/W2799758613","https://openalex.org/W2808498263","https://openalex.org/W2840169530","https://openalex.org/W2890840584","https://openalex.org/W2894906112","https://openalex.org/W2906631928","https://openalex.org/W2912386632","https://openalex.org/W2917792613","https://openalex.org/W2946584829","https://openalex.org/W2947279351","https://openalex.org/W2948974578","https://openalex.org/W2950763986","https://openalex.org/W2959003478","https://openalex.org/W2962723986","https://openalex.org/W2962814013","https://openalex.org/W2963070905","https://openalex.org/W2963078860","https://openalex.org/W2963341924","https://openalex.org/W2963516518","https://openalex.org/W2963680240","https://openalex.org/W2964105864","https://openalex.org/W2964112702","https://openalex.org/W2964249870","https://openalex.org/W2981707695","https://openalex.org/W2986604550","https://openalex.org/W3009213340","https://openalex.org/W3098486933","https://openalex.org/W3099486271","https://openalex.org/W3108009835","https://openalex.org/W3111160948","https://openalex.org/W3116271006","https://openalex.org/W3122864121","https://openalex.org/W6675354045","https://openalex.org/W6681968150","https://openalex.org/W6717697761","https://openalex.org/W6720057410","https://openalex.org/W6728920836","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6751281049","https://openalex.org/W6754805829","https://openalex.org/W6763253128","https://openalex.org/W6763315676","https://openalex.org/W6775841296","https://openalex.org/W6786587134"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W4312467842","https://openalex.org/W4363671829","https://openalex.org/W4287995534","https://openalex.org/W2998168123","https://openalex.org/W3116554004","https://openalex.org/W3135121883","https://openalex.org/W3017266184","https://openalex.org/W3210448364","https://openalex.org/W4301188316"],"abstract_inverted_index":{"Anomaly":[0,91],"detection":[1,132,201],"in":[2,25,40,77,184],"encrypted":[3],"traffic":[4,30,183],"is":[5,79,179,211],"a":[6,44,63,67,80,88,128,173,185,192,217],"growing":[7],"problem,":[8],"and":[9,31,116,119,140,213],"many":[10],"approaches":[11,20,56],"have":[12,203],"been":[13],"proposed":[14,235],"to":[15,22,36,61,71,99,167,180,198,215],"solve":[16],"it.":[17],"However,":[18],"those":[19],"need":[21],"be":[23],"trained":[24],"the":[26,52,136,143,152,164,169,223,231],"massive":[27,218],"of":[28,54,145,220,233],"normal":[29],"specific-class":[32],"abnormal":[33],"traffic,":[34],"so":[35],"achieve":[37],"good":[38],"results":[39,229],"that":[41],"specific-class.":[42],"For":[43],"new":[45,74,170,224],"anomaly":[46,69,75,131,182,188,200,225],"class":[47,171,189],"with":[48,95,190],"few":[49,68,174,193],"labeled":[50],"samples,":[51],"effectiveness":[53,232],"existing":[55],"will":[57,202],"decline":[58],"sharply.":[59],"How":[60],"train":[62],"model":[64,166],"using":[65,122],"only":[66,191],"samples":[70],"detect":[72,181],"unseen":[73,187],"classes":[76],"training":[78,138],"huge":[81,204],"challenge.":[82,102],"In":[83],"this":[84,101],"paper,":[85],"we":[86],"propose":[87],"Few-shot":[89],"Class-adaptive":[90],"Detection":[92],"framework":[93],"(FCAD)":[94],"model-agnostic":[96],"meta-learning":[97],"(MAML)":[98],"meet":[100],"Given":[103],"an":[104],"input":[105],"network":[106],"flow,":[107],"FCAD":[108,126,162],"first":[109],"extracts":[110],"statistical":[111],"features":[112,121],"by":[113,172],"feature":[114,117],"extractor":[115],"selector,":[118],"time-series":[120],"LSTM-based":[123],"AutoEncoder.":[124],"Then,":[125],"designs":[127],"MAML-based":[129],"few-shot":[130,153,199],"model,":[133],"relying":[134],"on":[135],"episodic":[137],"paradigm":[139],"learning":[141],"from":[142],"collection":[144],"K-way-M-shot":[146],"classification":[147],"tasks,":[148],"which":[149],"can":[150],"mimic":[151],"regime":[154],"faced":[155],"at":[156],"test":[157],"time":[158],"during":[159],"training.":[160],"Finally,":[161],"uses":[163],"pre-trained":[165],"adapt":[168],"iterations":[175],"steps.":[176],"Our":[177],"goal":[178],"before":[186],"samples.":[194],"A":[195],"reliable":[196],"solution":[197],"potential":[205],"for":[206],"real-world":[207],"applications":[208],"since":[209],"it":[210],"expensive":[212],"arduous":[214],"collect":[216],"amount":[219],"data":[221],"onto":[222],"class;":[226],"extensive":[227],"experimental":[228],"demonstrate":[230],"our":[234],"approach.":[236]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}